Extracting relationships from unstructured text remains a challenging problem because conventional approaches work based on either using manually-defined extraction rules, which tend to be very brittle, or learning relationships from text, which usually requires 1000s of hand-curated training examples. Unstructured text such as, but not limited to, unstructured log files, industrial asset shop visit reports, electronic medical data is not annotated. Having a domain expert manually annotate 1000s of examples from such datasets to develop new extraction rules is extremely time consuming and costly.
It would therefore be desirable to provide a system to extract relationships from unstructured text that requires neither 1000s of hand-curated examples nor text mining expertise.